Docker Containers for ONNX Runtime

Dockerfiles

• CPU Dockerfile, Instructions
• CUDA + CUDNN: Dockerfile, Instructions
• nGraph: Dockerfile, Instructions
• TensorRT: Dockerfile, Instructions
• OpenVINO: Dockerfile, Instructions
• Nuphar: Dockerfile, Instructions
• ARM 32v7: Dockerfile, Instructions
• ONNX-Ecosystem (CPU + Converters): Dockerfile, Instructions
• ONNX Runtime Server: Dockerfile, Instructions

Published Microsoft Container Registry (MCR) Images

Use docker pull with any of the images and tags below to pull an image and try for yourself. Note that the CPU, CUDA, and TensorRT images include additional dependencies like miniconda for compatibility with AzureML image deployment.

Example: Run docker pull mcr.microsoft.com/azureml/onnxruntime:latest-cuda to pull the latest released docker image with ONNX Runtime GPU, CUDA, and CUDNN support.

Building and using Docker images

CPU

Ubuntu 16.04, CPU, Python Bindings

1. Build the docker image from the Dockerfile in this repository.

docker build -t onnxruntime-source -f Dockerfile.source .

2. Run the Docker image

docker run -it onnxruntime-source

CUDA

Ubuntu 16.04, CUDA 10.0, CuDNN 7

1. Build the docker image from the Dockerfile in this repository.

docker build -t onnxruntime-cuda -f Dockerfile.cuda .

2. Run the Docker image

docker run -it onnxruntime-cuda

nGraph

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Ubuntu 16.04, Python Bindings

1. Build the docker image from the Dockerfile in this repository.

docker build -t onnxruntime-ngraph -f Dockerfile.ngraph .

2. Run the Docker image

docker run -it onnxruntime-ngraph

TensorRT

Ubuntu 16.04, TensorRT 5.0.2

1. Build the docker image from the Dockerfile in this repository.

docker build -t onnxruntime-trt -f Dockerfile.tensorrt .

2. Run the Docker image

docker run -it onnxruntime-trt

OpenVINO

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Ubuntu 16.04, Python Bindings

1. Build the onnxruntime image for one of the accelerators supported below.

   Retrieve your docker image in one of the following ways.

   • To build your docker image, download the OpenVINO online installer version 2019 R1.1 for Linux from this link and copy the OpenVINO tar file to the same directory before building the Docker image. The online installer size is 16MB and the components needed for the accelerators are mentioned in the dockerfile. Providing the docker build argument DEVICE enables the onnxruntime build for that particular device. You can also provide arguments ONNXRUNTIME_REPO and ONNXRUNTIME_BRANCH to test that particular repo and branch. Default repository is http://github.com/microsoft/onnxruntime and default branch is master.

     docker build -t onnxruntime --build-arg DEVICE=$DEVICE .
     
   • Pull the official image from DockerHub.

2. DEVICE: Specifies the hardware target for building OpenVINO Execution Provider. Below are the options for different Intel target devices.

   Device Option	Target Device
   CPU_FP32	Intel CPUs
   GPU_FP32	Intel Integrated Graphics
   GPU_FP16	Intel Integrated Graphics
   MYRIAD_FP16	Intel MovidiusTM USB sticks
   VAD-M_FP16	Intel Vision Accelerator Design based on MovidiusTM MyriadX VPUs

OpenVINO on CPU

1. Retrieve your docker image in one of the following ways.

   • Build the docker image from the DockerFile in this repository.

     docker build -t onnxruntime-cpu --build-arg DEVICE=CPU_FP32 --network host .
     

   • Pull the official image from DockerHub.

     # Will be available with next release
     

2. Run the docker image

    docker run -it onnxruntime-cpu
   

OpenVINO on GPU

1. Retrieve your docker image in one of the following ways.

   • Build the docker image from the DockerFile in this repository.

      docker build -t onnxruntime-gpu --build-arg DEVICE=GPU_FP32 --network host .
     
   • Pull the official image from DockerHub.

       # Will be available with next release
     

2. Run the docker image

   docker run -it --device /dev/dri:/dev/dri onnxruntime-gpu:latest
   

OpenVINO on Myriad VPU Accelerator

1. Retrieve your docker image in one of the following ways.
   • Build the docker image from the DockerFile in this repository.

      docker build -t onnxruntime-myriad --build-arg DEVICE=MYRIAD_FP16 --network host .
     
   • Pull the official image from DockerHub.

      # Will be available with next release
     
2. Install the Myriad rules drivers on the host machine according to the reference in here
3. Run the docker image by mounting the device drivers

   docker run -it --network host --privileged -v /dev:/dev  onnxruntime-myriad:latest
   
   

OpenVINO on VAD-M Accelerator Version

1. Retrieve your docker image in one of the following ways.
   • Build the docker image from the DockerFile in this repository.

      docker build -t onnxruntime-vadr --build-arg DEVICE=VAD-M_FP16 --network host .
     
   • Pull the official image from DockerHub.

      # Will be available with next release
     
2. Install the HDDL drivers on the host machine according to the reference in here
3. Run the docker image by mounting the device drivers

   docker run -it --device --mount type=bind,source=/var/tmp,destination=/var/tmp --device /dev/ion:/dev/ion  onnxruntime-hddl:latest
   
   

ARM 32v7

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The Dockerfile used in these instructions specifically targets Raspberry Pi 3/3+ running Raspbian Stretch. The same approach should work for other ARM devices, but may require some changes to the Dockerfile such as choosing a different base image (Line 0: FROM ...).

1. Install DockerCE on your development machine by following the instructions here

2. Create an empty local directory

   mkdir onnx-build
   cd onnx-build
   

3. Save the Dockerfile to your new directory

   • Dockerfile.arm32v7

4. Run docker build

   This will build all the dependencies first, then build ONNX Runtime and its Python bindings. This will take several hours.

   docker build -t onnxruntime-arm32v7 -f Dockerfile.arm32v7 .
   

5. Note the full path of the .whl file

   • Reported at the end of the build, after the # Build Output line.
   • It should follow the format onnxruntime-0.3.0-cp35-cp35m-linux_armv7l.whl, but version number may have changed. You'll use this path to extract the wheel file later.

6. Check that the build succeeded

   Upon completion, you should see an image tagged onnxruntime-arm32v7 in your list of docker images:

   docker images
   

7. Extract the Python wheel file from the docker image

   (Update the path/version of the .whl file with the one noted in step 5)

   docker create -ti --name onnxruntime_temp onnxruntime-arm32v7 bash
   docker cp onnxruntime_temp:/code/onnxruntime/build/Linux/MinSizeRel/dist/onnxruntime-0.3.0-cp35-cp35m-linux_armv7l.whl .
   docker rm -fv onnxruntime_temp
   

   This will save a copy of the wheel file, onnxruntime-0.3.0-cp35-cp35m-linux_armv7l.whl, to your working directory on your host machine.

8. Copy the wheel file (onnxruntime-0.3.0-cp35-cp35m-linux_armv7l.whl) to your Raspberry Pi or other ARM device

9. On device, install the ONNX Runtime wheel file

   sudo apt-get update
   sudo apt-get install -y python3 python3-pip
   pip3 install numpy
   
   # Install ONNX Runtime
   # Important: Update path/version to match the name and location of your .whl file
   pip3 install onnxruntime-0.3.0-cp35-cp35m-linux_armv7l.whl
   

10. Test installation by following the instructions here

Nuphar

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Ubuntu 16.04, Python Bindings

1. Build the docker image from the Dockerfile in this repository.

docker build -t onnxruntime-nuphar -f Dockerfile.nuphar .

2. Run the Docker image

docker run -it onnxruntime-nuphar

ONNX Runtime Server

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Ubuntu 16.04

1. Build the docker image from the Dockerfile in this repository

docker build -t {docker_image_name} -f Dockerfile.server .

2. Run the ONNXRuntime server with the image created in step 1

docker run -v {localModelAbsoluteFolder}:{dockerModelAbsoluteFolder} -p {your_local_port}:8001 {imageName} --model_path {dockerModelAbsolutePath}

3. Send HTTP requests to the container running ONNX Runtime Server

Send HTTP requests to the docker container through the binding local port. Here is the full usage document.

curl  -X POST -d "@request.json" -H "Content-Type: application/json" http://0.0.0.0:{your_local_port}/v1/models/mymodel/versions/3:predict